Abstract: Because there are many parameters of grain temperature model and determination of these parameters is difficult. This paper proposes an optimization method based on PSO for grain temperature model. The experimental results show that the model with optimized parameters by this paper’s methods can calculate more accurate the temperature of grain than existing methods. The error ratio to real measurement data is significantly decreased. This method can optimize many parameters of models in complex conditions. The accuracy can be significantly improved.

Key words: Particle Swarm Optimization（PSO）, model of temperature field for grain, ascertain the values of parameters

摘要： 针对粮堆温度模型中的众多参数难以确定的问题，提出基于粒子群算法的粮堆温度模型参数优化方案。实验结果表明，模型应用经过优化的参数能准确地计算出粮堆内部温度，与实际测量温度误差不大。该方法可以优化复杂条件下模型的众多参数，提高整个模型的精确度。

关键词: 粒子群算法, 粮堆温度场模型, 参数取值